How to Use the Northflank (Developer Cloud & Orchestration) MCP in LlamaIndex

Q: Can LlamaIndex help me find which Northflank projects are using a specific secret?

Yes. By regularly running listprojects and listsecrets for each, LlamaIndex indexes this data via this MCP server. You can then query your agent to find all projects associated with a particular secret group.

Q: How would I use Northflank (Developer Cloud & Orchestration) and LlamaIndex to debug a deployment?

Your agent can getservice to fetch the current state and listjobs to check for related failures. Since LlamaIndex remembers past states, you can ask it "What's changed since the last successful deployment?" to quickly spot the problem.

Q: Can I restart a service directly from my LlamaIndex query?

Yes, the tool specification includes restartservice. Your agent can decide to call this tool based on your query or its own analysis of the indexed data, and this MCP server provides the tools to do it.

Turn your Northflank infrastructure into a queryable knowledge base with LlamaIndex. Ask questions about your deployments, get real answers.

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Connect Northflank (Developer Cloud & Orchestration) MCP to LlamaIndex

Create your Vinkius account to connect Northflank (Developer Cloud & Orchestration) to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Northflank (Developer Cloud & Orchestration) with LlamaIndex

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Index Your Cloud State for RAG

LlamaIndex doesn't just call tools; it remembers the results. Run `list_projects`, `list_services`, and `list_jobs`, and the output is automatically indexed into a vector store. Now your infrastructure is a searchable document. Ask questions like "Which services are running in the staging project?" or "What cron jobs ran last night?". LlamaIndex finds the relevant indexed data from past `list_services` or `list_jobs` calls and uses it to give you a precise, grounded answer.

Build Self-Documenting Infrastructure with LlamaIndex

Every time you provision a new environment with `create_project` or check its configuration with `get_project`, the details are captured. Your knowledge base grows with every action taken by the MCP server. This creates a living document of your cloud setup. New team members can query the index to understand how services are configured or what secrets are available via `list_secrets` without having to dig through dashboards or code.

Query Deployment History and Status

After you `trigger_build` for a new release, the outcome and metadata are indexed. Over time, you build a searchable history of all deployments. You can then ask your LlamaIndex agent complex questions like "Compare the service configuration before and after the last deployment." It uses the indexed results from `get_service` calls to provide a diff, something a simple CLI tool can't do.

Setup guide

Set up Northflank (Developer Cloud & Orchestration) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Northflank (Developer Cloud & Orchestration) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Northflank (Developer Cloud & Orchestration) tools.",
)
response = await agent.run("List recent Northflank (Developer Cloud & Orchestration) data")

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Common questions about Northflank (Developer Cloud & Orchestration) MCP in LlamaIndex

Yes. By regularly running `list_projects` and `list_secrets` for each, LlamaIndex indexes this data via this MCP server. You can then query your agent to find all projects associated with a particular secret group.

Your agent can `get_service` to fetch the current state and `list_jobs` to check for related failures. Since LlamaIndex remembers past states, you can ask it "What's changed since the last successful deployment?" to quickly spot the problem.

That's exactly what this is for. Your agent can fetch live data using tools like `list_services` and combine it with your indexed history to answer questions with up-to-the-minute accuracy. This MCP server connects the two.

Yes, the tool specification includes `restart_service`. Your agent can decide to call this tool based on your query or its own analysis of the indexed data, and this MCP server provides the tools to do it.

The server only handles data relevant to your commands, such as project identifiers, service statuses, and secret names (not their values). Each request is handled in a dedicated, zero-trust container that is destroyed after use, and your API key is managed by Vinkius.

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